I was wondering what frames of view others had on how they use the new AI wave to augment themselves in some way or another. I realize that itself is somewhat vague, since you could take “augment” to mean anything from, i.e. in writing, prose review to full-on generation of articles or whatnot. The sense I mean particularly is that where it facilitates something in/by you, not necessarily just for you.

I’ve taken to using LLMs as sort of “problem space fuzzers”. I know for a lot of people, myself included, learning something new feels a lot more natural when’s it’s by doing rather than simply reading/reciting (where possible). Sometimes, while a discipline itself interests me, I can’t find the initial angle to crack it at where it sparks my curiosity to do its own thing. To that end I’m finding that it’s not a bad idea to set an LLM loose on some related project even if you may not be fully understanding everything it’s doing - with a caveat. Continually observing and steering the thread of development gives you a view of how the concepts map to each other as parts of a whole, enough for you to have an initial working model to do more yourself. Sure, you could look at an existing open-source repo if one exists (or any other sort of documentation), but I feel this approach gives the initial learning curve the feel of “doing” as well, rather than that “doing” being locked behind some prerequisite reading/recital.

Interested to hear how others approach this thought. Likewise interested in the relevant views of those who don’t personally see AI now or ever as an augment to their lives, and what drives that view.